Intelligent systems that act, not just answer

What we build

The AI & Machine Learning practice covers the full span of applied AI described in the Hayati NextGen capability matrix:

• LLM applications — drafting, summarization, question answering, and search grounded in a trusted corpus through retrieval-augmented generation (RAG), so answers cite your content rather than the model's memory.
• AI agents and agentic workflows — systems that plan and take multi-step actions: calling tools, querying internal systems, and handing off to humans where judgment is required.
• Conversational and voice AI — customer-facing and internal assistants designed around escalation paths, guardrails, and measurable quality.
• Computer vision, OCR, and intelligent document processing — turning documents, images, and forms into structured, usable data.
• Recommendation and predictive analytics — models that support decisions using your operational data.

How engagements typically work

AI work at Hayati NextGen starts from the operational problem — the process, the data, the decision — rather than from a model. Engagements generally move through understanding the workflow, establishing what data exists and its quality, selecting the approach (an off-the-shelf model, a fine-tuned model, or a retrieval-grounded system), building an evaluation harness before scaling, and integrating with the systems people already use.

Engineering discipline

Production AI requires more than a model: evaluation sets, monitoring of model behavior after deployment, data versioning, and a retraining or content -refresh cycle. These are part of the delivery model, not an optional extra.